System and method for visualization of ultrasound volumes

ABSTRACT

A method for assisted reading of automated ultrasound image volumes includes receiving a plurality of scan images generated from an imaging device, wherein the plurality of scan images comprises a chest wall region. The method further includes determining a chest wall model representative of the chest wall region based on the plurality of scan images. The method also includes determining a plurality of segmented scan images segmented along the chest wall region based on the chest wall model. In addition, the method includes determining lesion information using an automated lesion detection technique applied to the plurality of segmented scan images. The method also includes displaying the plurality of scan images along with at least one of the lesion information and the chest wall model.

BACKGROUND

Embodiments of the present specification relate generally tovisualization of image volumes, and more particularly to systems andmethods for assisted reading of automated ultrasound volumes.

Imaging modalities such as Computed Tomography (CT), Magnetic ResonanceImaging (MRI), and Ultrasound (US) are configurable to acquire imagedata sets corresponding to internal structures and tissues of a subjectfor medical diagnosis and treatment. In recent years, advancedvisualizing technology has been used to view complex three-dimensional(3D) structures inside the subject that are otherwise difficult to studyvia standard slice images. For example, rendering of image volumes andtime indexed data sets corresponding to the subject are widely used inmedical diagnosis.

Further, while rendering an affected region in the subject, where theaffected region includes a plurality of objects of interest, displayingsurrounding tissues provides a positional relationship, therebyenhancing understanding of users (e.g., clinicians, medicalpractitioners, and the like) carrying out the medical diagnosis. Forimproved visualization, it is highly desirable that shapes of theplurality of objects of interest are clearly reproduced simultaneouslyin one image. Also, better visualization helps the users to effectivelyassess, diagnose and select treatment options. Moreover, enhancedrendering techniques also help patients in understanding the medicalcondition and providing informed consent for suggested medicalprocedures. Breast cancer is one of the leading causes of cancer relateddeaths in women across the world and early detection plays an importantrole in effective management of the disease. The use of ultrasoundimaging as a breast cancer screening tool is increasing steadily due torelative cost advantage and patient comfort considerations. Also,ultrasound images may provide improved detection sensitivity in specificsections of populations such as young women with relatively dense breasttissue.

Known methods for detecting lesions in ultrasound images of the breasthave some disadvantages. For example, scanning the patient with theultrasound probe is highly operator dependent, which may result ininconsistent and inaccurate ultrasound scans. Moreover, relatively lowquality of ultrasound images and the addition of artifacts such asspeckle noise, shadows, ringing, and the like may increase thedifficulty of lesion detection within ultrasound images.

Automated breast ultrasound (ABUS) scan volume are often acquired fromvarious angles. However, utilizing the redundancy information in thesevolumes is a challenge. Also, the inclusion of the non-breast regionssuch as the ribs and chest wall confounds the detection of lesions bothby the clinicians and computed assisted design (CAD) algorithms.Additionally, automated breast scan volumes are typically voluminous innature and medical personnel require machine assistance in examining thedata set.

BRIEF DESCRIPTION

In accordance with one aspect of present specification, a method isdisclosed. The method includes receiving a plurality of scan imagesgenerated from an imaging device. The plurality of scan images comprisesa chest wall region. The method further includes determining a chestwall model representative of the chest wall region based on theplurality of scan images. The method also includes determining aplurality of segmented scan images segmented along the chest wall regionbased on the chest wall model. In addition, the method includesdetermining lesion information using an automated lesion detectiontechnique applied to the plurality of segmented scan images. The methodalso includes displaying the plurality of scan images along with atleast one of the lesion information and the chest wall model.

In accordance with another aspect of the present specification, a systemis disclosed. The system includes an imaging device configured togenerate a plurality of scan images, wherein the plurality of scanimages comprises a chest wall region. The system further includes achest wall detector unit communicatively coupled to the imaging deviceand configured to generate a chest wall model based on the plurality ofscan images. The system also includes a segmentation unitcommunicatively coupled to the chest wall detector unit and configuredto segment the plurality of scan images along a boundary of the chestwall to determine a plurality of segmented scan images. The systemfurther includes lesion detector unit communicatively coupled to thesegmentation unit and configured to generate lesion information in theplurality of scan images. The system also includes a display unitcommunicatively coupled to the lesion detector unit and configured todisplay the plurality of scan images along with at least one of thelesion information and the chest wall model.

In accordance with another aspect of the present specification, anon-transitory computer readable medium having instructions isdisclosed. The instructions enable at least one processor to receive aplurality of scan images generated from an imaging device, wherein theplurality of scan images comprises a chest wall region. The instructionsfurther enable the at least one processor to determine a chest wallmodel representative of the chest wall region based on the plurality ofscan images. The instructions further enables the at least one processorto determine a plurality of segmented scan images segmented along thechest wall region based on the chest wall model. The instructions alsoenable the at least one processor to determine lesion information usingan automated lesion detection technique applied to the plurality ofsegmented scan images. In addition, the instructions enable the at leastone processor to display the plurality of scan images along with atleast one of the lesion information and the chest wall model.

DRAWINGS

These and other features and aspects of embodiments of the presentinvention will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 is a diagrammatic illustration of a system for visualization ofultrasound volumes, in accordance with aspects of the presentspecification;

FIG. 2 is a flow chart illustrating a method for visualization ofultrasound volumes, in accordance with aspects of the presentspecification;

FIG. 3 illustrates front view of an ellipsoid model covering the ribcage, in accordance with aspects of the present specification;

FIG. 4 illustrates a side view of the ellipsoid model covering the ribcage, in accordance with aspects of the present specification;

FIG. 5 is a lateral view of an image illustrating effectiveness of adirect registration technique, in accordance with aspects of the presentspecification;

FIG. 6 is a lateral view of an image illustrating effectiveness of atwo-step registration technique in accordance with aspects of thepresent specification;

FIG. 7 is an image illustrating a true positive case of lesiondetection, in accordance with aspects of present specification;

FIG. 8 is an image illustrating a false positive case of lesiondetection, in accordance with aspects of the present specification; and

FIG. 9 is a flow chart of a method for visualization of ultrasoundvolumes in accordance with aspects of the present specification.

DETAILED DESCRIPTION

As will be described in detail hereinafter, systems and methodsconfigured for visualization of ultrasound volumes are presented. Moreparticularly, the systems and methods are configured for assistedreading of automated ultrasound volumes, such as breast ultrasoundvolumes.

FIG. 1 is a diagrammatic illustration of an imaging system 100 having anenhanced visualization capability, in accordance with one aspect of thepresent specification. In one embodiment, the imaging system 100 is anultrasound system, although other embodiments of imaging system includea magnetic resonance imaging (MRI) system and a computer tomography (CT)imaging system. The imaging system 100 includes an imaging device 104such as ultrasound scanning device configured to acquire a plurality ofimage volumes 106 from a subject 102 during medical examination. In oneembodiment, the plurality of image volumes includes a left scan volumecorresponding to an image volume generated from scanning a left portionof the breast, a right scan volume corresponding to an image volumegenerated from scanning a right portion of the breast, and a center scanvolume corresponding to an image volume generated from scanning thecenter portion of the breast. In this embodiment, the plurality of imagevolumes 106 corresponds to automated breast ultrasound (ABUS) imagevolumes.

The imaging system 100 further includes a visualization subsystem 108communicatively coupled to the imaging device 104 and configured toreceive the plurality of image volumes 106. The visualization subsystem108 is further configured to process the plurality of image volumes 106to generate additional information helpful for assisted reading of imagevolumes and provide a visualization output 126. In one embodiment, thevisualization subsystem 108 is configured to determine at least one of arib information, a chest wall information, and a lesion informationbased on the ABUS and render the information for assisting medicalpractitioners. In a presently contemplated configuration, thevisualization subsystem 108 includes a chest wall detector unit 110, asegmentation unit 112, a lesion detector unit 114, a display unit 116, amemory unit 118, and a processor unit 120 communicatively coupled toeach other through a communication bus 124.

The chest wall detector unit 110 is communicatively coupled to theimaging device 104 and configured to receive the plurality of imagevolumes 106. Each of the plurality of image volumes 106 includes aplurality of scan images. The plurality of scan images includes a chestwall region. The chest wall detector unit 110 is configured to determinea chest wall model representative of the chest wall region based on theplurality of scan images. In one embodiment, the plurality of scanimages corresponding to one of the image volumes is processed. In oneembodiment, the chest wall detector unit 110 is configured to identifyribs in the chest region based on the plurality of scan images. Inanother embodiment, the chest wall detector unit 110 is configured todetermine a chest wall surface based on the plurality of scan images. Inone embodiment, a rib centerline is extracted based on a recursivetracing technique. In another embodiment, the rib information isobtained using orientation space filtering. In yet another embodiment, agradient ring feature map is obtained for determining the chest wallsurface in the plurality of scan images.

In certain embodiments, the chest wall is detected based on a two-stepregistration technique. The two-step registration technique uses anatlas (or a template image) representative of a chest wall model. In thefirst step of the two-step registration technique, the atlas isinitialized for registration with the plurality of scan images. In oneembodiment, the atlas may be determined offline using a plurality ofimage volumes previously acquired and stored in the memory unit 118. Theinitialization of atlas includes a rough segmentation of one or more ofthe plurality of scan images and a rigid registration with the atlas.The deformations obtained from the registration are incorporated intothe atlas to complete the initialization. In the second step, adeformable registration technique is used to align the atlas with one ormore of the plurality of scan images. The deformable registrationtechnique may include scaling and rotation of the atlas for obtaining abest overlap with one or more of the plurality of scan images.

The chest wall detector unit 110 is further configured to use aplurality of scan volumes sets generated from the plurality of scans forrefining the chest wall model. Each scan volume set of the plurality ofscan volumes sets includes a left scan volume, right scan volume, and acenter scan volume. In an alternate embodiment, the plurality of scanvolumes sets is used to refine the template image. In one embodiment, amachine learning technique may be used to refine the chest wall model orthe template image. The machine algorithm may use parameters such as,but not limited to, rib spacing, rib size, and average tissue depthextracted from the plurality of scan volume sets for refining the chestwall model or the template image.

The segmentation unit 112 is communicatively coupled to the chest walldetector unit 110 and configured to provide a plurality of segmentedscan images 122. In one embodiment, the segmentation is performed usinga graph cut segmentation technique. In another embodiment, thesegmentation is performed based on a top-down segmentation technique. Inthis embodiment, the boundaries of one or more of the plurality of scanimages is deformed to provide a best overlap with the template image. Inone embodiment, determining the plurality of segmented scan images 122includes determining a surface representative of a boundary of the chestwall region.

The lesion detector unit 114 is communicatively coupled to thesegmentation unit 112 and configured to detect lesions in one or more ofthe plurality of scan images. In one embodiment, the lesions aredetected in a region anterior to the chest wall region. In such anembodiment, the region below the chest wall region in the scan images isremoved before applying a lesion detection technique. In anotherembodiment, the lesions are detected below the chest wall region. In oneembodiment, the lesion detector unit 114 determines the lesions based onan automatic lesion detection technique without requiring assistancefrom a user. In an alternative embodiment, a user assisted segmentationtechnique may be used to determine the lesions in one or more of theplurality of scan images.

The display unit 116 is communicatively coupled to the lesion detectorunit 114 and configured to display the automated breast ultrasound imagevolumes and provide visualization output 126 to a user, such as aphysician. In one embodiment, the ultrasound images are displayed alongwith chest wall information, ribs information, and the lesioninformation. In an alternative embodiment, at least one of the chestwall information, ribs information, and the lesion information isdisplayed along with the ultrasound image. In one embodiment, thephysician is provided with an option to view one or more of theplurality of scan images without any additional information. In oneembodiment, lesion information above the chest wall is displayed and thelesion information below the chest wall is not displayed, therebyreducing the false positives in the displayed lesions in the ultrasoundimages.

The memory unit 118 is communicatively coupled to the communication bus124 and may be accessed by one or more of the chest wall detector unit110, the segmentation unit 112, the lesion detector unit 114, and thedisplay unit 116. In an exemplary embodiment, the memory unit 118 mayinclude one or more memory modules. The memory unit 118 may be anon-transitory storage medium. For example, the memory may be a dynamicrandom access memory (DRAM) device, a static random access memory (SRAM)device, flash memory, or other memory devices. In one embodiment, thememory may include a non-volatile memory or similar permanent storagedevice, media such as a hard disk drive, a floppy disk drive, a compactdisc read only memory (CD-ROM) device, a digital versatile disc readonly memory (DVD-ROM) device, a digital versatile disc random accessmemory (DVD-RAM) device, a digital versatile disc rewritable (DVD-RW)device, a flash memory device, or other non-volatile storage devices. Inanother embodiment, a non-transitory computer readable medium may beencoded with a program composed of instructions to instruct theprocessor unit 120 to perform functions of the chest wall detector unit110, the segmentation unit 112, the lesion detector unit 114, and thedisplay unit 116.

The processor unit 120 is communicatively coupled to the memory unit 118and may include at least one of an arithmetic logic unit, amicroprocessor, a general purpose controller, and a processor array toperform the desired computations or run the computer programs. In oneembodiment, the processor unit 120 may be configured to aid the chestwall detector unit 110, the segmentation unit 112, the lesion detectorunit 114, and the display unit 116 in performing associated tasks. Itmay be noted that while the embodiment of FIG. 1 depicts the processorunit 120 as a separate unit, in certain embodiments, the chest walldetector unit 110, the segmentation unit 112, the lesion detector unit114, and the display unit 116 may include a corresponding processorunit.

FIG. 2 is a flow chart 200 illustrating a method for visualization ofultrasound volumes, in accordance with aspects of the presentspecification. At step 202, a plurality of image volumes correspondingto an automated breast ultrasound (ABUS) volume, is received from anultrasound scanning device. A chest wall model is determined at step 204based on a plurality of scan images extracted from the plurality ofimage volumes. Further, at step 206, a segmentation of the chest wallregion is performed based on the plurality of scan images and the chestwall model to determine a plurality of segmented scan images. Anellipsoid model is determined for a plurality of points on a surface ofthe chest wall, at step 222. At step 224, lesion information isdetermined based on the plurality of segmented scan images. The lesioninformation includes location and dimensions of one or more lesionsdetected by a lesion detection technique applied to each of theplurality of segmented scan images. At step 226, the lesions in a leftscan volume, a right scan volume, and a center scan volume are alignedto provide view correspondence. Subsequently, at step 228, an assistedreading of the ABUS volume is provided to a physician by displaying atleast one of the chest wall information, lesion information, and ribinformation along with scan images having view correspondence betweenthe left scan, right scan, and center scan volumes.

In one embodiment of step 204, determination of the chest wall modelincludes processing the plurality of scan images to enhance densetissues based on a filtering based technique, as illustrated in step208. In one embodiment, a Hessian filter is used to process theplurality of scan images to generate a plurality of enhanced images. TheHessian filter is a square matrix of second order partial derivatives ofa scalar valued function. The Hessian filter is configured to enhancetissues related to local structures of the plurality of image volumesbased on relationship of Eigen values of the Hessian matrix. In oneembodiment, the local structures include, but are not limited to, atube-like object, a blob-like object, and a sheet-like object. Athreshold is applied to each of the plurality of enhanced images todetermine candidate pixels corresponding to the dense tissues. In oneembodiment, the threshold is adaptively selected for optimallydetermining the candidate pixels.

Further, a rough depth map is determined based on the candidate pixelsrepresentative of dense tissues in the plurality of scan images. In oneembodiment, the depth map is representative of a distance from the skinto pixels on a coronal plane of an image (also referred to as a coronalimage). A quadratic surface representative of the chest wall isdetermined based on the depth map. In one embodiment, the quadraticsurface is a cloud of points on the chest wall surface. Further, acoronal image is also determined based on the quadratic surface. In oneembodiment, the coronal image is determined by an averaging operationapplied to the plurality of scan images with reference to the quadraticsurface. As an example, each pixel in the coronal image is determined asan average of pixels of the scan image within a determined distance fromthe location of the quadratic surface. In one example, the determineddistance may be about 5 mm.

At step 210, a rib like region is determined in the plurality of scanimages. In addition, at step 210, a rib centerline information isextracted. In one embodiment, an orientation space filtering is used todetermine rib like regions. The orientation filter generates ananisotropic response in images having tube like structures. In oneembodiment, the orientation filter is used to filter the coronal imageto generate an orientation image. In one embodiment, rib centerlineinformation is determined based on the orientation image. The ribcenterline information also includes orientation information from noisein the coronal image. In another embodiment, a rib is modelled as aBezier curve having three control points. For each candidate centerlinein the rib centerline information, a cost is determined based on theintensity of the control points associated with the candidate. Thecandidates having costs lower than a pre-determined centerline thresholdare considered as rib centerlines. In another embodiment, a lengthrestriction is also imposed on the Bezier curve to exclude candidatecenterlines that are not related to ribs. In another embodiment, thecandidate centerlines which are parallel are considered as ribcenterlines.

Further, a gradient ring feature map is generated at step 212. In oneembodiment, a first gradient operator is determined along an x-axis anda second gradient operator is determined along a y-axis. A firstgradient image is determined by processing a sample image using thefirst gradient operator. A second gradient image is determined byprocessing the sample image using the second gradient operator. Further,a feature map is determined based on the first gradient image and thesecond gradient image. In general, a plurality of gradient images may bedetermined along a plurality of directions and the feature map isdetermined based on the plurality of gradient images. In one embodiment,the feature map includes a plurality of feature response valuesdetermined based on a direction of gradient corresponding to the ribregion. The rib information is determined based on the value of thefeature response. It may be noted herein that the feature responseincludes most salient features and is not affected by speckle noise.

In another embodiment of step 204, an atlas is initialized at step 216.The atlas refers to a chest wall model corresponding to previouslyacquired breast ultrasound volumes. In one embodiment, an average chestwall model is determined from known chest wall models corresponding tothe previously acquired ultrasound volumes. In another embodiment, apre-determined chest wall model is retrieved from the memory unit. Step216 is the first step of the two-step registration technique. Further,at step 218, the pre-determined chest wall model is registered to eachof the images of the plurality of images. The registration at step 218is a deformable registration that is used to generate a plurality ofregistered images. The deformable registration step involves one or moreof rotation, translation, and scaling of each of the plurality ofimages. The deformable registration also includes modifying theboundaries of an image to have a best match with the atlas. Step 218 isthe second step of the two-step registration technique.

In one embodiment of the segmentation step 206, a bottom up segmentationtechnique is used (step 214) to determine the plurality of segmentedscan images segmented along the chest wall region based on the chestwall model. Image segmentation refers to partitioning of an image intomultiple segments with pixels in each segment sharing commoncharacteristics. In the bottom-up segmentation of step 214, similarsmaller portions related to a single object within a scan image areidentified. Further, similar portions are combined to form an imagesegment. In one embodiment of the bottom-up segmentation, a graph cutsegmentation is used. In such an embodiment, an image is considered as agraph with pixels corresponding to nodes and a link between each pair ofpixels in the image has a weight representative of a similarity betweenpixels of the pair. A graph having ordered nodes is referred to as adirected graph. By way of example, an s-t graph is a directed graph witha source node s and a sink node t. An s-t cut c(s, t) in a graph is aset of links E such that there is no path from the source s to sink twhen E is removed from the graph. The cost of a cut E is the sum of theweights of links in the set E. It is desirable to minimize c(s, t). Inparticular, it is desirable to determine an s-t cut having a minimalcost c(s, t). The graph cut segmentation technique is equivalent toconstructing a graph corresponding to an image such that the minimal cutof this graph segments the image.

In another embodiment of the segmentation step 206, a top-downsegmentation technique is performed at step 220 for each of theplurality of scan images obtained from the step 218 to determine theplurality of segmented scan images. In one embodiment, the top-downsegmentation technique includes dividing each of the plurality of scanimages obtained from the step 218 recursively into smaller portions tosegment the image along the pre-determined chest wall model.

At step 222, an ellipsoid model is conformed to the quadratic surfacedetermined at step 208. To that end, first an ellipsoid model templateis determined based on an average chest size from biometry dataavailable from offline experimentation and analysis. Alternatively, theellipsoid model template is determined based on archived ultrasoundimages. An optimization technique is used to register the ellipsoidmodel template to the quadratic surface. A cost function representativeof a sum of the shortest distances from points on the quadratic surfaceto the ellipsoid model is used by the optimization technique. In oneembodiment, a six degree of freedom is allowed to the points on thequadratic surface. The six degrees of freedom correspond to translationsand rotations about three orthogonal axes. The optimization techniqueprovides a transformation of the points to the ellipsoid model. In oneembodiment, an inverse of the transformation is used for transferringthe ellipsoid model onto the image space. Specifically, the inverse ofthe transformation is used to determine location and orientation of theellipsoid model in the image space.

In another embodiment, a point cloud model of the chest wall isdetermined based on the computed tomography (CT) volumes of the chest.In one embodiment, the point cloud model may be a model templaterequiring registration and inversion. In another embodiment, the pointcloud model may be specific to the subject under consideration.

Further, at step 224, a lesion detection technique is used to determinelesion information in the plurality of scan images. In one embodiment,the lesion detection is performed based on an automated technique. Inanother embodiment, the lesion detection is performed based on asemi-automatic technique that entails manual intervention. A pluralityof techniques involving learning algorithms and classifier models areused for lesion detection in the plurality of scan images extracted fromthe ultrasound volumes and generation of lesion information.

In one embodiment, at step 226, one of the plurality of ultrasound imagevolumes is synchronized with the rest of the plurality of ultrasoundimage volumes. Specifically, a correspondence between the left scanvolume, the right scan volume, and the center scan volume is establishedbased on at least one fiducial feature in the plurality of ultrasoundimage volumes. In one embodiment, a nipple location, identified in oneof the steps of 208, 210, 212, may be used as the fiducial feature forestablishing image correspondence across the image volumes. In oneembodiment, establishing the correspondence includes selecting a firstscan volume and a second scan volume different from the first scanvolume selected from the left scan volume, the right scan volume, andthe center scan volume. A fiducial feature is identified both in thefirst scan volume and the second scan volume. The correspondence betweenthe first scan volume and the second scan volume is established in twosub steps. In first sub step, the fiducial feature in the first scanvolume is registered with the second scan volume using a rigidregistration technique. In second sub step, tissue features of the firstscan volume are registered with the second scan volume using adeformable registration technique. In some embodiments, at least one ofthe left scan volume and the right scan volume is combined with thecenter scan view. In such embodiments, a left scan image from the leftscan volume is selected. A right scan image from the right scan volumeand a center scan image from the center scan volume corresponding to theleft scan image are also selected. Further, an overlap region betweenone of the left scan images and the right scan image with the centerscan image is identified. A combined image is generated by fusing thecenter scan image with one of the left scan image and the right scanimage. In one example of fusing the center scan image with the left scanimage, contents of one of the left scan image and the center scan imageis retained in the overlapped region. The choice of the image to beretained in the overlapping region is controlled by the user duringimage display.

In an alternative embodiment, image volumes across imaging modalitiesmay be synchronized at step 226. In particular, a plurality of imagevolumes corresponds to imaging modalities such as, but not limited to,CT, MRI, and Digital Breast Tomosynthesis (DBT). In one embodiment, atleast one of the rib information, lesion information, or chest wallinformation determined using image volumes corresponding to one imagingmodality may be transferred onto the image volumes generated fromanother imaging modality. An image fiducial feature such as the nipplelocation may be used to transfer at least one of the lesion information,chest wall information, and rib information across the image volumesobtained from different imaging modalities.

FIG. 3 illustrates a front view 300 of an ellipsoid model covering a ribcage 302, in accordance with one aspect of the present specification.The front view 300 illustrates the rib cage 302 and an ellipse 304surrounding the rib cage 302. The ellipse 304 is a front view of anellipsoid model for the chest wall region. Dimensions of the ellipsoidare selected such that a sum of the distances of the rib points from theellipsoid surface is minimized.

FIG. 4 illustrates a side view 400 of an ellipsoid model covering a ribcage 402, in accordance with one aspect of the present specification.The side view 400 illustrates the rib cage 402 and an ellipse 404surrounding the rib cage 402. The ellipse 404 is a side view of anellipsoid model for modelling the chest wall region. Dimensions of theellipsoid are selected such that a sum of the distances of the ribpoints from the ellipsoid surface is minimized.

FIG. 5 is a lateral view of an image 500 illustrating effectiveness of aconventional direct registration technique. The image 500 includes ahull 504 from an atlas and an actual chest wall 502 from an ultrasoundimage. The image 500 shows registration of the hull 504 to the actualchest wall 502 using the direct registration technique. It may beobserved that the registration is poor in the initial portion.

FIG. 6 is a lateral view of an image 600 illustrating effectiveness of atwo-step registration technique in accordance with aspects of presentspecification. The image 600 includes a hull 604 from an atlas and anactual chest wall 602 from an ultrasound image. The image 600 showsregistration of the hull 604 to the actual chest wall 602 using thetwo-step registration technique. As illustrated, advantageously, thetwo-step registration technique provides enhanced registration along aplurality of points of the chest wall.

FIG. 7 is an image 700 illustrating a true positive case of lesiondetection, in accordance with aspects of present specification. Theimage 700 illustrates a lesion at point 702 detected using an automaticlesion detection technique. The image 700 also includes a chest wallboundary 704 identified using a chest wall model. The detected lesion atpoint 702 lies above the chest wall boundary 704 and is representativeof a true positive case.

FIG. 8 is an image 800 illustrating a false positive case of lesiondetection, in accordance with aspects of present specification. Theimage 800 illustrates a lesion at point 802 detected using an automaticlesion detection technique. The image 800 also includes a chest wallboundary 804 identified using a chest wall model. The detected lesion atpoint 802 lies below the chest wall boundary 804 and is representativeof a false positive case.

FIG. 9 is a flow chart 900 of a method for visualization of ultrasoundvolumes, in accordance with aspects of the present specification. Atstep 902, the method includes receiving a plurality of scan imagesgenerated from an ultrasound device configured to examine a patient. Theplurality of scan images is extracted from one of a left scan volume, aright scan volume, and a center scan volume and includes a chest wallregion. The method further includes determining a chest wall modelrepresentative of the chest wall region based on the plurality of scanimages, as indicated by step 904. In one embodiment, determining thechest wall model includes determining a plurality of pointsrepresentative of the chest wall region. In another embodiment,determining the chest wall model includes determining an ellipsoid modelfor the chest wall region. In yet another embodiment, determining thechest wall model also includes determining rib information in the chestwall region. In another embodiment of determining the chest wall, atemplate image is aligned with each of the plurality of scan imagesusing a deformable registration technique. In one embodiment ofdetermining the rib information, a rib centerline information isdetermined based on an orientation filtering technique. In anotherembodiment of determining the rib information, a gradient ring featuremap is generated based on the gradient information representative ofsalient features of rib cage in the chest wall region.

At step 906, the method also includes determining a plurality ofsegmented scan images segmented along the chest wall region based on thechest wall model. In one embodiment, a bottom-up segmentation techniqueis used for generating the plurality of segmented scan images. Inanother embodiment, each of the plurality of scan images is aligned withthe template image using a top-down segmentation technique. At step 908,the method includes determining lesion information using an automatedlesion detection technique applied to the plurality of segmented scanimages. Further, at step 910, the method includes displaying theplurality of scan images along with at least one of the lesioninformation and the chest wall model. In one embodiment, displayingincludes establishing correspondence between at least two of the leftscan volume, the right scan volume, and the center scan volume using afiducial feature in the plurality of scan images. In another embodiment,displaying includes transferring at least one of the visualizing lesioninformation and the chest wall information corresponding to a pluralityof scan images obtained from another imaging modality using a fiducialfeature.

Embodiments of systems and methods disclosed herein help in reducingfalse positives during automatic lesion detection in ABUS volumes.Further, disclosed technique provides flexibility of selecting one ormore computer aided visualization options to enhance the confidence ofboth expert and novice users during assisted reading of ABUS volumes.

It is to be understood that not necessarily all such objects oradvantages described above may be achieved in accordance with anyparticular embodiment. Thus, for example, those skilled in the art willrecognize that the systems and techniques described herein may beembodied or carried out in a manner that achieves or improves oneadvantage or group of advantages as taught herein without necessarilyachieving other objects or advantages as may be taught or suggestedherein.

While the technology has been described in detail in connection withonly a limited number of embodiments, it should be readily understoodthat the specification is not limited to such disclosed embodiments.Rather, the technology can be modified to incorporate any number ofvariations, alterations, substitutions or equivalent arrangements notheretofore described, but which are commensurate with the spirit andscope of the claims. Additionally, while various embodiments of thetechnology have been described, it is to be understood that aspects ofthe specification may include only some of the described embodiments.Accordingly, the specification is not to be seen as limited by theforegoing description, but is only limited by the scope of the appendedclaims.

1. A method, comprising: receiving a plurality of scan images generated from an imaging device, wherein the plurality of scan images comprises a chest wall region; determining a chest wall model representative of the chest wall region based on the plurality of scan images; determining a plurality of segmented scan images segmented along the chest wall region based on the chest wall model; determining lesion information using an automated lesion detection technique applied to the plurality of segmented scan images; and displaying the plurality of scan images along with at least one of the lesion information and the chest wall model.
 2. The method of claim 1, further comprising receiving a left scan volume, a right scan volume, and a center scan volume, wherein each of the left scan volume, the right scan volume, and the center scan volume comprises the plurality of scan images.
 3. The method of claim 2, wherein displaying the plurality of segmented scan images comprises establishing correspondence between at least two of the left scan volume, the right scan volume, and the center scan volume using a fiducial feature in the plurality of scan images.
 4. The method of claim 1, wherein determining the chest wall model comprises determining a plurality of points representative of the chest wall region.
 5. The method of claim 1, wherein determining the chest wall model comprises determining an ellipsoid model for the chest wall region.
 6. The method of claim 1, wherein determining the chest wall model comprises determining rib information in the chest wall region.
 7. The method of claim 1, wherein determining the chest wall model comprises determining a rib centerline information.
 8. The method of claim 1, wherein determining the chest wall model comprises generating a gradient ring feature map.
 9. The method of claim 1, wherein determining the chest wall model comprises generating a template image representative of the chest wall model.
 10. The method of claim 9, further comprising matching the template image to the plurality of scan images based on a deformable registration technique.
 11. The method of claim 1, wherein determining the plurality of segmented scan images comprises determining a surface representative of a boundary of the chest wall region.
 12. The method of claim 1, wherein determining the plurality of segmented scan images is based on a bottom-up segmentation technique or a top-down segmentation technique.
 13. The method of claim 1, wherein determining lesion information comprises applying a lesion detection technique to the chest wall region of a segmented scan image.
 14. The method of claim 1, wherein displaying the plurality of scan images comprises displaying the plurality of scan images along with at least one of rib information, the chest wall model, and the lesion information.
 15. A system, comprising: an imaging device configured to generate a plurality of scan images, wherein the plurality of scan images comprises a chest wall region; a chest wall detector unit communicatively coupled to the imaging device and configured to generate a chest wall model based on the plurality of scan images; a segmentation unit communicatively coupled to the chest wall detector unit and configured to segment the plurality of scan images along a boundary of the chest wall to determine a plurality of segmented scan images; a lesion detector unit communicatively coupled to the segmentation unit and configured to generate lesion information in the plurality of scan images; and a display unit communicatively coupled to the lesion detector unit and configured to display the plurality of scan images along with at least one of the lesion information and the chest wall model.
 16. The system of claim 15, wherein the imaging device is further configured to generate a left scan volume, a right scan volume, and a center scan volume, and wherein the left scan volume, the right scan volume, and the center scan volume comprise the plurality of scan images.
 17. The system of claim 16, wherein the display unit is further configured to establish correspondence between at least two of the left scan volume, the right scan volume, and the center scan volume using a fiducial feature.
 18. The system of claim 15, wherein the chest wall detector unit is configured to generate a plurality of points representative of the chest wall region.
 19. The system of claim 15, wherein the chest wall detector unit is configured to determine an ellipsoid model for the chest wall region.
 20. The system of claim 15, wherein the chest wall detector unit is configured to determine rib information in the chest wall region.
 21. The system of claim 15, wherein the chest wall detector unit is configured to determine a rib centerline information.
 22. The system of claim 15, wherein the chest wall detector unit is configured to generate a gradient ring feature map.
 23. The system of claim 15, wherein the chest wall detector unit is configured to generate a template image representative of a chest wall model.
 24. The system of claim 23, wherein the chest wall detector unit is configured to match the template image to the plurality of scan images based on a deformable registration technique.
 25. The system of claim 15, wherein the segmentation unit is configured to determine a surface representative of boundary of the chest wall region.
 26. The system of claim 15, wherein the segmentation unit is configured to perform image segmentation based on a bottom-up segmentation technique or a top-down segmentation technique.
 27. The system of claim 15, wherein the lesion detector unit is configured to perform lesion detection in a region anterior to the chest wall within a segmented scan image.
 28. The system of claim 15, wherein display unit is configured to display the plurality of scan images along with at least one of the chest wall model, the lesion information.
 29. A non-transitory computer readable medium having instructions to enable at least one processor to: receive a plurality of scan images generated from an imaging device, wherein the plurality of scan images comprises a chest wall region; determine a chest wall model representative of the chest wall region based on the plurality of scan images; determine a plurality of segmented scan images segmented along the chest wall region based on the chest wall model; determine lesion information using an automated lesion detection technique applied to the plurality of segmented scan images; and display the plurality of scan images along with at least one of the lesion information and the chest wall model. 